Open AccessA Convolutional Neural Network for Improved Anomaly-Based Network Intrusion Detection
Author(s) -
Isra Al-Turaiki,
Najwa Altwaijry
Publication year - 2021
Publication title -
big data
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.774
H-Index - 27
eISSN - 2167-647X
pISSN - 2167-6461
DOI - 10.1089/big.2020.0263
Subject(s) - computer science , convolutional neural network , artificial intelligence , deep learning , benchmark (surveying) , intrusion detection system , data mining , network security , feature engineering , machine learning , anomaly detection , dimensionality reduction , preprocessor , data set , artificial neural network , feature (linguistics) , network architecture , computer security , linguistics , philosophy , geodesy , geography
Cybersecurity protects and recovers computer systems and networks from cyber attacks. The importance of cybersecurity is growing commensurately with people's increasing reliance on technology. An anomaly detection-based network intrusion detection system is essential to any security framework within a computer network. In this article, we propose two models based on deep learning to address the binary and multiclass classification of network attacks. We use a convolutional neural network architecture for our models. In addition, a hybrid two-step preprocessing approach is proposed to generate meaningful features. The proposed approach combines dimensionality reduction and feature engineering using deep feature synthesis. The performance of our models is evaluated using two benchmark data sets, namely the network security laboratory-knowledge discovery in databases data set and the University of New South Wales Network Based 2015 data set. The performance is compared with similar deep learning approaches in the literature, as well as state-of-the-art classification models. Experimental results show that our models achieve good performance in terms of accuracy and recall, outperforming similar models in the literature.